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Please use this identifier to cite or link to this item:
http://krishi.icar.gov.in/jspui/handle/123456789/13423
Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Sandip Shil, Kishore K. Das, and Ananta Sarkar | en_US |
dc.date.accessioned | 2018-11-26T08:50:11Z | - |
dc.date.available | 2018-11-26T08:50:11Z | - |
dc.date.issued | 2016-04-01 | - |
dc.identifier.citation | Not Available | en_US |
dc.identifier.issn | 2070-5948 | - |
dc.identifier.uri | http://krishi.icar.gov.in/jspui/handle/123456789/13423 | - |
dc.description | Not Available | en_US |
dc.description.abstract | Normalization of gene expression data refers the process of minimizing non-biological variation in measured probe intensity levels so that biological differences in gene expression can be appropriately detected. Several linear normalization within arrays approaches have already been proposed. Recently, use of non-linear methods has been gained quite attention. In this study, our objective is to formulate non-linear normalization methods using support vector regression (SVR) and support vector machine quantile regression (SVMQR) approaches more easier way and, assess the consistency of these methods with respect to other standard ones for further application in gene expression data. After implementation, the performances of SVR and SVMQR have been compared with respect to other standard normalization methods namely, locally weighted scatter plot smoothing and kernel regression. The results indicate that the normalized data based on proposed methods are capable of producing minimum variances within replicate groups and, also able to detect truly expressible significant genes compared to above mentioned other normalized data. | en_US |
dc.description.sponsorship | Not Available | en_US |
dc.language.iso | English | en_US |
dc.publisher | Electronic Journal of Applied Statistical Analysis | en_US |
dc.relation.ispartofseries | Not Available; | - |
dc.subject | support vector machine quantile regression, support vector regression, normalization methods, microarray, intensity level. | en_US |
dc.title | Normalization of gene expression data using support vector machine approach | en_US |
dc.title.alternative | Not Available | en_US |
dc.type | Journal | en_US |
dc.publication.projectcode | Not Available | en_US |
dc.publication.journalname | Electronic Journal of Applied Statistical Analysis | en_US |
dc.publication.volumeno | 9(1) | en_US |
dc.publication.pagenumber | 95-110 | en_US |
dc.publication.divisionUnit | Not Available | en_US |
dc.publication.sourceUrl | DOI: 10.1285/i20705948v9n1p95 | en_US |
dc.publication.authorAffiliation | ICAR::Central Plantation Crops Research Institute | en_US |
dc.ICARdataUseLicence | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf | en_US |
Appears in Collections: | HS-CPCRI-Publication |
Files in This Item:
File | Description | Size | Format | |
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15026-119971-1-PB.pdf | 1.4 MB | Adobe PDF | View/Open |
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